LARGE-SCALE MAPPING OF FLOOD USING SENTINEL-1 RADAR REMOTE SENSING

Abstract

Sentinel-1 Synthetic Aperture Radar (SAR), with its extensive coverage and regular data acquisition all over the globe, has become one of the most valuable assets for flood monitoring in recent years. However, the strong influence of incidence angle on backscatter measurement of Sentinel-1 data makes it challenging to mosaic Sentinel-1 tracks for systematic flood mapping over large areas. This study uses a cosine squared normalization of Sentinel-1 data based on Lamberts law for optics to homogenize SAR data from different tracks. Then, it combines normalized data from ascending and descending passes forms 12-day mosaics covering Bangladesh from January 2017 to December 2021. Afterward, it estimates flood evolution by segmentation of the country-wide mosaics of data and calculates a flood frequency map. The flood frequency map, along with the population information, is then used to estimate the flood risk to the Bangladesh population. The results show that normalization can reduce inconsistencies between different tracks of Sentinel-1 data. Furthermore, it shows the potential of Sentinel-1 data for systematic flood mapping at large scales. Such analysis can help implement flood management measures on a national scale to reduce the flood risk.